The aviation community faces increasing flight delays, security concerns and airline costs. Industry stakeholders such as the Federal Aviation Administration (FAA), the airlines, and the Transportation Security Agency operate in a complex real-time environment with layered dependencies that make the outcome of air traffic management initiatives hard to predict. Thus, planning of air traffic initiatives in more detail, and further in advance, such that the national airspace system can be managed more efficiently has become increasingly important. One key requirement for enacting an air traffic system with higher emphasis on strategic management of traffic is accurately predicting air traffic demand within various airspaces.
Controlled airspaces are typically subdivided into a number of sectors, and generally an individual air traffic controller is responsible for controlling air traffic within a particular sector. The number of flights expected to be in a particular sector during a time period of interest is the demand for that sector. Since one air traffic controller can reasonably be expected to monitor and direct only a limited number of flights (e.g., 10 to 15 flights) at the same time within the sector for which they are responsible, it is desirable to determine the expected demand within sectors of a controlled airspace and the effect that an individual flight request will have on the expected demand at some time in the future so that the flights within an airspace can be directed appropriately to help keep the anticipated number of flights within the sectors of the airspace within manageable levels. A limited number of systems and methods are currently applied to the problem of air traffic demand predictions. One example of such a system is the FAA's enhanced air traffic management system (ETMS). However, many of these methods and systems are not sufficiently accurate, particularly under non-standard environments, such as convective weather situations, in order to effectively predict air traffic demand.